United States

While machine learning (ML) models of today have the potential to be useful in many societal applications, they also harbor the potential for great harm, be it perpetuating biases or compromising privacy. To prevent these harms, many (evolving) regulatory guardrails have been put in place; for instance European Union&#39;s GDPR and Biden&#39;s Executive Order which demand explainability, privacy, fairness and so on from models deployed in societal applications. Yet, most technical solutions in the Trustworthy ML literature which claim to meet these regulatory requirements are brittle and often fail at the task in hand. To this end, my research aims to make the field of Trustworthy ML reliable using mainstay concepts of Measurement, Mitigation and Maintenance. With these concepts, I develop end-to-end solutions for trustworthy ML by (1) exploring the limitations of existing approaches and (2) providing principled novel solutions exploiting interconnections with cryptography.

AAAI 2025

The Mainstays of Trustworthy Machine Learning

While machine learning (ML) models of today have the potential to be useful in many societal applications, they also harbor the potential for great harm, be it perpetuating biases or compromising privacy. To prevent these harms, many (evolving) regulatory guardrails have been put in place; for instance European Union's GDPR and Biden's Executive Order which demand explainability, privacy, fairness and so on from models deployed in societal applications. Yet, most technical solutions in the Trustworthy ML literature which claim to meet these regulatory requirements are brittle and often fail at the task in hand. To this end, my research aims to make the field of Trustworthy ML reliable using mainstay concepts of Measurement, Mitigation and Maintenance. With these concepts, I develop end-to-end solutions for trustworthy ML by (1) exploring the limitations of existing approaches and (2) providing principled novel solutions exploiting interconnections with cryptography.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Integrating Large Language Models (LLMs) into software engineering unlocks new opportunities to automate manual processes but raises challenges around reliability, safety, and scalability. My research centers on this synergy, with two key objectives: first, harnessing LLMs to solve software engineering tasks traditionally dependent on labor-intensive, domain-specific methods, and second, applying robust software engineering principles to improve LLM safety and performance. This dual focus creates a powerful feedback loop, where LLMs drive innovation while engineering rigor ensures these systems meet the high standards required for real-world applications.

Advancing Intelligent Software Development and Trustworthy Models Through the Synergy of Software Engineering and LLMs

Foundation models in general domains have leveraged multimodal knowledge graphs to great effect, yet the healthcare sector lacks such comprehensive structures, presenting a significant gap in current research. Based on previous exploration with pure data-driven approaches, this proposal describes a two-stage project aiming to enhance multimodal healthcare foundation model with domain knowledge. The first stage is to construct a robust multimodal healthcare knowledge graph based on established healthcare taxonomies, such as UMLS, and enriched with data from multimodal clinical databases like MIMIC-CXR. This knowledge graph will incorporate medical images as cross-modal instances linked to healthcare terminologies, enhancing the depth and applicability of the graph. In the second stage, the knowledge graph will serve as a foundational tool in training healthcare foundation models with enhanced capabilities, particularly in reducing hallucination and managing concept ambiguity through the novel use of reinforcement learning techniques like Direct Preference Optimization (DPO). This research is expected to make significant contributions to the domain of healthcare AI by enabling more accurate, reliable, and explainable AI-driven diagnostics and interventions.

Developing Multimodal Healthcare Foundation Model: From Data-driven to Knowledge-enhanced

Synthesizing electronic health records (EHR) is essential for addressing data scarcity, bias, and fairness in healthcare models. EHR data are inherently multimodal and sequential, encompassing structured codes, clinical notes, medical
images, and irregular time intervals. Traditional generative models like GANs and VAEs struggle to capture these complexities, while diffusion-based models offer improvements but remain limited to task-specific applications. To address these challenges, two diffusion-based models, MedDiffusion and EHRPD, have been developed. MedDiffusion enhances health risk prediction by generating synthetic patient data
and capturing visit-level relationships, while EHRPD generates sequential, multimodal EHR data, incorporating temporal interval estimation to improve diversity and fidelity. Future work aims to overcome limitations in multimodal data generation by developing a generalized model capable of handling diverse modalities simultaneously, expanding the applicability of EHR data generation across healthcare tasks.

Advancing Medical Multimodal Learning and Data Generation with Diffusion Model and LLM

While advances in machine learning and the expansion of massive datasets have significantly improved predictive accuracy, the translation of these predictions into actionable decisions—alongside a robust understanding of associated risks—remains underexplored. My research focuses on developing methodology and theory in data-driven decision-making and uncertainty quantification that effectively address core data challenges. This paper presents two connected pillars of my research: data-driven contextual optimization, uncertainty quantification and reduction.

Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning

Economic growth and development require a consistent supply of energy. Energy which has mainly been supplied from fossil fuels. The impacts of these on the environment such as global warming, raised an alarm on their use. As a result other sources of energy such as wind energy are used as alternatives for electricity production. Wind energy assessment nevertheless faces barriers due to its stochastic nature. This later creates various regimes, which traditional models can't always fit thereby producing poor estimates. In this work, we aim to use Large Language Models (LLMs) to predict the wind potential in a given location. Through this approach, we aim at lifting the barrier on energy problems in developing countries by providing knowledge on the state of wind energy in given locations.

Leveraging Large Language Models for Wind Energy Assessment

Deploying machine learning (ML) models in high-stakes domains such as healthcare and autonomous systems requires reliable uncertainty quantification (UQ) to ensure safe and accurate decision-making. 
Conformal prediction (CP) offers a robust, distribution-agnostic framework for UQ, providing valid prediction sets that guarantee a specified coverage probability. 
However, existing CP methods are often limited by assumptions that are violated in real-world scenarios, such as non-i.i.d. data, and by a lack of integration with modern machine learning workflows, particularly in large generative models. 
This research aims to address these limitations by advancing CP techniques to operate effectively in non-i.i.d. settings, improving predictive efficiency without sacrificing theoretical guarantees, and integrating CP directly into model training processes. 
These developments will enhance the practical applicability of CP for a wide range of ML tasks, enabling more reliable and interpretable models in high-stakes applications.

Reliable Uncertainty Quantification in Machine Learning via Conformal Prediction

Efficient human-agent collaboration requires understanding each other’s capabilities and establishing appropriate reliance. My thesis focuses on optimizing performance in mixed-initiative settings, where humans and agents dynamically contribute to decisions and actions. I first explore key factors shaping human reliance on decision-support agents, then examine how agents can model this reliance to initiate actions. My proposed work aims to enable agents to jointly provide decision and action support in multi-objective tasks, using bi-directional communication to enhance collaboration.

Adaptive Agents for Mixed-Initiative Human-AI Collaborations

Despite significant advancements in solving Markov Decision Processes (MDPs) and Simple Stochastic Games (SGs), scalability remains a challenge due to the exponential growth of their state spaces. This thesis aims to push the boundaries of state-of-the-art methods by tackling this issue using 1) explainability and 2) exploiting the model structure. First, we introduce the *1-2-3-Go* approach, which learns explainable policies from small MDP models and generalizes them to larger instances, improving scalability in MDPs. We then extend *Optimistic Value Iteration (OVI)* and *Sound Value Iteration (SVI)*—originally designed for MDPs—to SGs, improving efficiency in adversarial settings. Finally, we aim to exploit the *explainable policy representations* and the *model structure* to enhance both scalability and interpretability in SGs. This thesis contributes to both theoretical advancements and practical solutions for decision-making systems under uncertainty.

Scalable Solutions for Decision-Making Systems Using Explainable Policy Representations

Large Language Models (LLMs) have shown promise in educational applications, but challenges such as hallucinations, lack of contextual relevance, and limited personalization impede their practical adoption. To address these issues, my research introduces MerryQuery, an LLM-powered educational agent that integrates Retrieval-Augmented Generation (RAG), rule-based content control, and Reinforcement Learning from Human Feedback (RLHF). The system features a dynamic learning profile module for adaptive personalization and a multi-step verification framework that cross-checks responses against external sources to enhance trustworthiness. A functional prototype of MerryQuery is being piloted in a real-world classroom. Preliminary results demonstrate improved response reliability and student understanding.

Developing LLM-Powered Trustworthy Agents for Personalized Learning Support

Autonomous robots are becoming more versatile and widespread in our daily lives. From autonomous vehicles to companion robots for senior care, these human-centric systems must demonstrate a high degree of reliability in order to build trust and, ultimately, deliver social value. How safe is safe enough for robots to be wholeheartedly trusted by society? Is it sufficient if an autonomous vehicle can avoid hitting a fallen cyclist 99.9% of the time? What if this rate can only be achieved by the vehicle always stopping and waiting for the human to move out of the way? I argue that, for trustworthy deployment of robots in human-populated space, we need to complement standard statistical methods with clear-cut robust safety assurances under a vetted set of operation conditions. We need runtime learning to minimize the robot’s performance loss during safety-enforcing maneuvers by reducing its inherent uncertainty induced by its human peers, for example, their intent (does a human driver want to merge, cut behind, or stay in the lane?) or response (if the robot comes closer, how will the human react?). We need to close the loop between the robot’s learning and decision-making so that it can optimize efficiency by anticipating how its ongoing interaction with the human may affect the evolving uncertainty, and ultimately, its long-term performance.

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Advancing Intelligent Software Development and Trustworthy Models Through the Synergy of Software Engineering and LLMs

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